利用系统生物学方法研究遗传和环境因素对肺癌发生的影响

In China, lung cancer has the highest death rate which increased 464.84% in last three decades. It is well known that environmental factors such as tobacco smoking and alcohol drinking are the crucial factors of lung cancer. However, only a few individuals suffer from the lung cancer while most of them are exposed in the identical circumstance. This is a possible indication that individualistic genetic factors besides environmental factors cause the occurrence and development of the cancer. Genome-wide association studies (GWAS) have to date been proved to be a effective and efficient approach to uncovered plenty of links between diseases and particular regions of the genome, but frustratingly haven't revealed much about the biochemistry behind these associations and haven't prevent the loss of discovering some disease susceptible loci due to it requires a higher significant signal (P-value ) for calling a susceptible loci of cancers. For this reason, here we aim to build local networks of interaction between SNPs and genes, instead of whole genome association analysis, reducing the need of high P-value to discover more susceptibility loci for lung cancer as well as its pathogenic mechanism. We will study the difference between lung cancer and normal on SNP, methylation, microRNA and gene expression level with systems biology method. A comprehensive network that includes eQTLs of SNP-gene/microRNA, SNP/methylation-gene/microRNA, and SNP/methylation-gene/microRNA, mQTL of methylation-SNP, and regulatory network of TF/microRNA-gene will be constructed. Furthermore, those SNPs obtained from lung cancer related SNPs in GWAS database and literature will be prioritized with network we built and their effect on methylation, microRNA, gene expression will be analyzed. What's more, novel method to infer the genotype and methylation based gene expression will be proposed, which can be used to predict the mechanism of lung cancer and factors that triggered the disease and to analyze the contributions of genetic and environment to disease.

肺癌是目前全球发病率和死亡率最高的肿瘤之一。在我国，肺癌居全部恶性肿瘤死因的首位，死亡率在近30年来上升了464.84%。环境暴露如吸烟、饮酒等是这些恶性肿瘤的重要病因，但相同环境暴露下仅有少数人发病，提示个体遗传因素也起着重要作用。目前，虽然全基因组关联研究（GWAS）被认为是研究包括肿瘤在内的复杂性疾病易感基因或致病基因的重要手段，但是GWAS不能揭示这些关联背后的生物学作用机制，并且由于GWAS发现易感位点需要很高的显著性(P-value)，导致一部分易感位点的遗失。因此，我们希望通过构建SNP与基因eQTL相互作用的局部网络，弥补GWAS的缺陷，发现更多的易感位点和治病基因以及治病机制。我们将采用系统生物学的方法来优化肺癌SNP的筛选，分析肺癌SNP对甲基化，microRNA和基因表达的影响，预测特定病人的发病机理及诱因，分析遗传和环境因素对发病的贡献程度。

肺癌是目前全球发病率和死亡率最高的肿瘤之一。在我国，肺癌居全部恶性肿瘤死因的首位，死亡率在近30 年来上升了464.84%。环境暴露如吸烟、饮酒等是这些恶性肿瘤的重要病因，但相同环境暴露下仅有少数人发病，提示个体遗传因素也起着重要作用。我们采用系统生物学网络分析和机器学习的方法，系统的研究了肺癌发生发展过程中遗传因素和环境因素的作用，构建了包含各种因素的肺癌整合调控网络，找到了肺癌的核心驱动因子，发现了PM 2.5诱导肺癌的机制，比较了非小细胞腺癌和鳞癌亚型的差别，找到了肺癌的候选治疗小分子化合物，共发表论文7篇。项目取得成果不仅加深了我们对肺癌发病机制的认识，而且对肺癌分型，精准诊断和治疗具有重要价值。